Machine Learning
1:
81-106,
1986
©
1986
Kluwer
Academic Publishers, Boston
-
Manufactured
in The
Netherlands
Induction
of
Decision Trees
J.R, QUINLAN (munnarilnswitgould.oz!quinlan@seismo,css.gov)
Centre
for
Advanced Computing Sciences,
New
South
Wales
Institute
of
Technology,
Sydney
2007,
Australia
(Received
August
1,
1985)
Key
words:
classification,
induction, decision trees,
information
theory,
knowledge
acquisition,
expert
systems
Abstract.
The
technology
for
building
knowledge-based
systems
by
inductive
inference
from
examples
has
been
demonstrated
successfully
in
several practical applications. This paper summarizes
an
approach
to
synthesizing
decision trees that
has
been used
in a
variety
of
systems,
and it
describes
one
such
system,
ID3,
in
detail. Results from recent studies show
ways
in
which
the
methodology
can be
modified
to
deal
with
information that
is
noisy
and/or
incomplete.
A
reported shortcoming
of the
basic algorithm
is
discussed
and two
means
of
overcoming
it are
compared.
The
paper concludes
with
illustrations
of
current
research directions.
1.
Introduction
Since
artificial
intelligence
first
achieved recognition
as a
discipline
in the mid
1950's,
machine
learning
has
been
a
central research
area.
Two
reasons
can be
given
for
this
prominence.
The
ability
to
learn
is a
hallmark
of
intelligent behavior,
so any
attempt
to
understand intelligence
as a
phenomenon must include
an
understanding
of
learn-
ing.
More
concretely, learning provides
a
potential
methodology
for
building high-
performance systems.
Research
on
learning
is
made
up of
diverse subfields.
At one
extreme there
are
adaptive systems that monitor their
own
performance
and
attempt
to
improve
it by
adjusting
internal parameters. This approach, characteristic
of a
large proportion
of
the
early learning work, produced self-improving programs
for
playing games
(Samuel, 1967), balancing poles (Michie, 1982), solving problems (Quinlan, 1969)
and
many other domains.
A
quite
different
approach sees learning
as the
acquisition
of
structured knowledge
in the
form
of
concepts (Hunt, 1962; Winston, 1975),
discrimination nets (Feigenbaum
and
Simon,
1963),
or
production rules (Buchanan,
1978).
The
practical
importance
of
machine learning
of
this
latter
kind
has
been underlin-
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资源评论
rexzhao2362011-10-09很经典的东西，全英文，值得阅读
